IWEPoissonPaper

IWEPoissonPaper models CpG island-wide DNA methylation dynamics in vertebrate genomes to capture interdependent methylation changes across clustered CpG sites and to infer branch lengths in cell type phylogenies.

Key Features:

• Probabilistic Modeling: Captures simultaneous methylation changes across multiple CpG sites within the same CpG island by modeling interdependence of methylation states.
• Markov-Chain Monte-Carlo (MCMC) Methodology: Employs MCMC to fit the probabilistic model to methylation data derived from cell type phylogenies.
• Inference of Phylogenetic Branch Lengths: Infers branch lengths in cell phylogenies based on methylation changes.
• Application to Murine Haematopoietic Cells: Has been applied to methylation data from murine haematopoietic cells to capture developmental methylation pattern changes.

Scientific Applications:

• Gene Regulation Studies: Supports analysis of how CpG island-wide methylation dynamics influence gene expression across cell types.
• Developmental Biology: Provides inference of lineage divergence and developmental processes via methylation-based branch length estimates.
• Epigenetics Research: Offers a framework for studying complex methylation patterns within CpG islands and their effects on cellular function and organismal development.
• Murine Haematopoiesis Analysis: Demonstrated utility on murine haematopoietic cell methylation datasets to study differentiation-associated methylation changes.

Methodology:

Integrates CpG island-wide probabilistic models of methylation dynamics and fits them to methylation data from cell type phylogenies using Markov-Chain Monte-Carlo (MCMC) inference.